Method and apparatus for interpreting information

ABSTRACT

A computer product for analyzing data representative of account usage, the computer product disposed on a computer readable medium and including instructions for causing a processor to provide a data classifier the data representative of account usage, wherein the data classifier produces in response to the data representative of account usage, classification outputs, provide to a rule inducer input data based on the data representative of account usage and the classification outputs, wherein the rule inducer produces rules descriptive of relationships between the data representative of account usage and the classification outputs. The data classifier can include a neural network and a Bayesian classifier. The input data based on the data representative of account usage can include a combination of the data representative of account usage and the classification outputs.

This application is a continuation application of U.S. Ser. No.08/840,115, filed on Apr. 15, 1997, naming Gary Howard, Paul C. Barson,Simon Field, and Philip W. Hobson as inventors, and now U.S. Pat. No.6,336,109, the contents of which are herein incorporated by reference intheir entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a method and apparatus for interpretinginformation and particularly for information relating to acommunications network.

2. Description of the Prior Art

In the telecommunications field, large amounts of data are available,for example about customer behaviour and telephone usage. This datacontains potentially useful information for many purposes such asdetection of fraud, marketing, billing, maintenance planning and faultdetection. However, the data must first be analysed in order to extractfeatures that can easily be used for a given task. This task ofextracting useful features from the data is often difficult because theuser does not know which type of features to look for. For example, theinformation may be in the form of call detail records (CDRs). A CDR is alog of an individual telephone call which contains information such asthe length of the telephone call, the customer account number, the typeof call and many other pieces of information. Over a given time periodmany CDRs will be recorded, each containing many different pieces ofinformation. When faced with this mass of information it can bedifficult to know what features to extract for a particular problem.

One possibility is to use a data classifier which searches for a set ofclasses and class descriptions that are most likely to explain a givendata set. Several types of such data classifiers are known. For example,Bayesian classifiers, neural network classifiers and rule basedclassifiers. For a given task, a classifier is typically trained on aseries of examples for the particular task. After the classifier hasbeen trained then new examples are presented to it for classification.The classifier can be trained either using a supervised method or anunsupervised method. In a supervised method the training examples thatare used are known examples. That is the user knows which classes thesetraining examples should be classified into and this information is alsoprovided to the classifier during the training phase. For unsupervisedtraining, there is no information about the desired classes for thetraining examples.

One problem is that the output of classifiers is often difficult tointerpret. This is especially the case when unsupervised training hasbeen used. The classifier output specifies which of a certain number ofclasses each input has been placed into. The user is given noexplanation of what the classes mean in terms of the particular task orproblem domain. Neither is the user provided with any information aboutwhy a particular input has been classified in the way that it has.

Previously, users have needed to carry out complex analyses of theclassifier in order to obtain these kinds of explanations. Knownexamples can be input to the classifier and the outputs compared withthe expected outputs. However, in order to do this known examples mustbe available and this is often not the case. Even when known examplescan be obtained this is often a lengthy and expensive procedure.

A further problem is that because these kinds of explanations are notavailable the user's confidence in the system is reduced. This meansthat the user is less likely to run the system, thus reducing the valueof such a system. Also, errors and mistakes are hard to detect. Forexample, if erroneous data is entered by mistake a resulting error inthe output could easily go unchecked. Similarly, if the trainingexamples were not representative of the example population for theparticular task then errors would be produced that would be hard tofind.

It is accordingly an object of the present invention to provide anapparatus and method for interpreting information relating to acommunications network which overcomes or at least mitigates one or moreof the problems noted above.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention there is provided amethod of processing data relating to a plurality of examples using adata classifier arranged to classify input data into one of a number ofclasses, and a rule inducer, comprising the steps of:

(i) inputting a series of inputs to the data classifier so as to obtaina series of corresponding outputs;

(ii) inputting said series of outputs and at least some of said seriesof inputs to the rule inducer so as to obtain a series of rules whichdescribe relationships between the series of inputs to the dataclassifier and the series of corresponding outputs from the dataclassifier. This provides the advantage that the rules can be used toprovide an explanation for the user about how the classification isperformed. Also, the rules can be used together with other informationabout the problem domain or task to help the user determine a “meaning”for each of the classes. Advantageously, the user's confidence in thesystem is increased and errors in the system can more easily be detectedand corrected.

Preferably, the data classifier is unsupervised. The output of anunsupervised classification system is especially difficult to interpret.Advantageously, the rules produced according to the invention can beused to help the user determine a “meaning” for the output of theunsupervised classifier.

Preferably, the method further comprises the step of transforming theseries of rules into a format such that the formatted rules can be usedas a data classifier. This provides the advantage that a rule basedclassifier can easily be created without the need for the user todetermine the rules directly from the data set or other data source.

Preferably the method further comprises the step of incorporating therules into a case-based reasoning system. This provides the advantagethat a case-base reasoning system can easily be created without the needfor the user to determine the rules directly from the data set or otherdata source. Advantageously the case-based reasoning system is able tolearn from new examples.

According to a second aspect of the present invention there is provideda method of processing data relating to a communications network using arule extractor and a neural network data classifier comprising the stepsof:

(i) inputting a series of training data inputs to the neural network andtraining the neural network using this series of training data so as toobtain a series of output values corresponding to the training datainputs;

(ii) inputting information about the configuration of the trained neuralnetwork to the rule extractor so as to obtain a series of rules whichdescribe relationships between the series of training data inputs andthe series of output values. This provides the advantage that the rulescan be used to provide an explanation for the user about how theclassification is performed. Also, the rules can be used together withother information about the problem domain or task to help the userdetermine a “meaning” for each of the classes. Advantageously, theuser's confidence in the system is increased and errors in the systemcan more easily be detected and corrected.

According to another aspect of the present invention there is provided acomputer system for processing data relating to a communications networkcomprising:

a data classifier arranged to classify input data into one of a numberof classes;

a rule inducer;

a first input arranged to accept a series of inputs to the dataclassifier;

a first output arranged to provide a series of corresponding outputsfrom the data classifier;

a second input arranged to accept said series of outputs and at leastsome of said series of inputs to the rule generator; and

a second output arranged to output from the rule generator a set ofrules which describe relationships between the series of inputs to thedata classifier and the series of corresponding outputs from the dataclassifier. This provides the advantage that the rules can be used toprovide an explanation for the user about how the classification isperformed. Also, the rules can be used together with other informationabout the problem domain or task to help the user determine a “meaning”for each of the classes. Advantageously, the user's confidence in thesystem is increased and errors in the system can more easily be detectedand corrected.

According to another aspect of the present invention there is provided acomputer system for processing data relating to a telecommunicationsnetwork comprising:

(i) a rule extractor;

(ii) a neural network data classifier;

(iii) a first input arranged to accept a series of training data inputsto the neural network;

(iv) a processor arranged to train the neural network using the seriesof training data inputs so as to produce a series of output valuescorresponding to the training data inputs; and

(v) a second input arranged to accept information about theconfiguration of the trained neural network to the rule extractor so asto produce a series of rules which describe relationships between theseries of training data inputs and the series of output values. Thisprovides the advantage that the rules can be used to provide anexplanation for the user about how the classification is performed.Also, the rules can be used together with other information about theproblem domain or task to help the user determine a “meaning” for eachof the classes. Advantageously, the user's confidence in the system isincreased and errors in the system can more easily be detected andcorrected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a general schematic diagram of an arrangement for interpretingdata.

FIG. 2 is a general schematic diagram indicating how a rule inducer anda data classifier are positioned in the arrangement of FIG. 1.

FIG. 3 is a general schematic diagram indicating how a rule extractorand a data classifier are positioned in the arrangement of FIG. 1according to another embodiment of the invention.

FIG. 4 shows the use of the invention for detecting and analysingtelecommunications fraud.

FIG. 5 shows example attributes.

FIG. 6 shows an example of input data for the rule inducer.

FIG. 7 shows an example of output from the rule inducer.

FIG. 8 shows the output of FIG. 7 incorporated into a rule-basedclassifier.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention are described below by way ofexample only. These examples represent the best ways of putting theinvention into practice that are currently known to the Applicantalthough they are not the only ways in which this could be achieved.

Definitions

rule extractor—any mechanism or technique for generating a set of rulesto describe the relationship between the inputs and outputs of a trainedneural network that use information about the weighted connections inthe neural network.

rule inducer—any mechanism or technique for generating rules to describea plurality of data that involves generalising from the data.

data classifier—any mechanism or technique for dividing or breaking up acollection of data into groups.

self organising map (SOM)—a neural network architecture which discoverspatterns in data by clustering similar inputs together. The data isgrouped by the SOM without any prior knowledge or assistance. Groupingis achieved by mapping the data onto a 2-D plane.

FIG. 1 shows a computer system 1 that is arranged to automaticallydetermine a classification system for a given data set that is providedto the system. The computer system accepts input data from a data source2. The computer system searches for a set of classes 3 and classdescriptions that are most likely to explain the provided data set. Oncethe classification system has been determined, new data can be input andclassified according to this system. For example, in a situation inwhich information about telephone calls needs to be analysed to detectfraud, the data source 2 consists of information about individualtelephone calls made during a certain time period. The computer system 1determines a classification system and classifies the calls into anumber of classes 3. Once this is done, a human operator or user thenanalyses the classes to see whether fraudulent calls appear only incertain classes. The user obtains an explanation of how that data fromthe data source 2 has been classified as well as an explanation of whatthe classes 3 mean in terms of the particular data source 2 and the taskor problem (e.g. fraud detection) involved.

In order to provide these explanations, FIG. 1 shows how the computersystem 1 is also arranged to produce rules 4 which describerelationships between the input data from the data source 2 and theclasses 3. Advantageously, these rules 4 can then be used to provide anexplanation of how the computer system 1 classified the input data 2.For example, such an explanation could be, “telephone call number 10 isa member of class 2 because it has feature A and feature B but notfeature C”. The rules 4 can also be used together with other informationto assign a “meaning” to the classes 3 that the input data 2 isclassified into. For example, classes could be assigned meanings such as“fraudulent examples” and “non-fraudulent examples”.

In one example, as shown in FIG. 2, the computer system 1 comprises adata classifier 21 and a rule inducer 25. A series of input data from adata source 22 is input to the data classifier 21 to produce acorresponding set of outputs 23. These outputs comprise informationabout which of a number of classes 23 each input is a member of. Theseries of input data from the data source 22 is also input to the ruleinducer 25 as indicated by arrow 26. The rule inducer 25 also receivesinformation about the corresponding series of outputs from the dataclassifier 21 as indicated by arrow 27. Given these inputs 26, 27 therule inducer 25 produces a series of rules 24 which describerelationships between the series of input data provided to the dataclassifier 21 and the corresponding series of outputs produced by thedata classifier.

In an alternative example a rule extractor is used instead of a ruleinducer. This is illustrated in FIG. 3. In this case the computer system1 comprises a neural network data classifier 31 and a rule extractor 35.A series of training data 32 is input to the neural network 31 and theneural network is trained using this input data. The neural network 31produces a series of outputs 33 or classes which correspond to theseries of training data. A description of the trained neural network 36is provided to the rule extractor 35 which is then able to produce aseries of rules 34. A description of the inputs 32 to the dataclassifier 31 may also be required as input to the rule extractor 35.These rules 34 describe relationships between the series of trainingdata 32 and the corresponding series of outputs 33 from the neuralnetwork. Any type of rule extractor can be used.

Once the rules 34, 24, 4 have been obtained they can also be used tocreate a rule-based classifier. This can then be used instead of or aswell as the data classifier 21, 31. The rules 34, 24, 4 can also beincorporated into a case-based reasoning system. A case-based reasoningsystem is advantageous in that it is able to learn by analogy.

A rule inducer is a fundamental component for a case-based reasoningsystem. Once the computer system 1 has been set up for a particularapplication, such as for telecommunications data, then the system 1 canbe incorporated into a case-based reasoning system. This enables acase-based reasoning system that is suitable for the particularapplication concerned to be set up quickly and easily.

The computer system 1 can be used to analyse data about the transmissionof messages in a communications network. For example, the use of thecomputer system 1 to interpret data about the performance of EDNA is nowdescribed.

EDNA is a system which organises and effects the distribution ofmessages in a network of computers. It is a UNIX based distributionframework and it can be used to distribute software and files. It canalso be used for system management, file collection and processautomation.

In this example, the task or aim is to investigate whether users of EDNAfall into distinct groups for billing purposes. If any groups are foundit is also desired to explain characteristics of the groups and relatethe groups to the problem domain.

In this example, the data source 2 comprises information about the useof EDNA over a certain time period. For each user of EDNA a list ofattribute values is given. A user of EDNA can be defined in differentways. For example, a user could correspond to a department in a workplace. It could comprise a number of different human users and/or nodesin the network. The list of attribute values for a user comprisesinformation such as the number of files transferred by the user duringthe time period. In this example, 6 attributes are used as shown in FIG.5. These include:

the number of EDNA transfers made during the time period 51;

the number of packages attempted during the time period 52;

the number of packages completed during the time period 53;

the number of links made during the time period 54;

the number of files transferred during the time period 55;

the total number of bytes transferred during the time period 56.

These attributes all relate to past usage of EDNA and comprise figuresindicating what EDNA has done over a certain time period.

This data about the use of EDNA is then classified using a dataclassifier 21 that automatically searches for a set of classes 3 andclass descriptions that are most likely to explain the data. Severaldifferent classifiers can be used for this. In this example the knownclassifier AUTOCLASS is used.

AUTOCLASS is an unsupervised classification system based on Bayesiantheory. It has been developed by P. Cheesman and his colleagues and isdescribed in the following documents which are intended to beincorporated herein by reference:

P. Cheesman, J. Stutz, “Bayesian Classification (AutoClass) Theory andResults,” in “Advances in Knowledge Discovery and Data mining”, U. M.Fayyad et al. Eds. The AAAI Press, Menlo Park, 1995.

R. Hanson, J. Stutz, P. Cheesman, “Bayesian Classification Theory”,Technical Report FIA-90-12-7-01, NASA Ames Research Centre, ArtificialIntelligence Branch, May 1991.

P. Cheesman, J. Kelly, M. Self, J. Stutz, W. Taylor, D. Freeman,“AutoClass: a Bayesian Classification system.” In proceedings of theFifth International Conference on Machine Learning, 1988.

P. Cheesman, M. Self, J. Kelly, J. Stutz, W. Taylor, D. Freeman,“Bayesian Classification.” In seventh National conference on ArtificialIntelligence, pages 607-611, Saint Paul, Minn., 1988.

AutoClass has been implemented in the C programming language and ispublicly available on the internet together with the following basic andsupporting documentation which is also incorporated herein by reference:

preparation-c.text

search-c.text

reports-c.text

interpretation-c.text

checkpoint-c.text

prediction-c.text

classes-c.text

models-c.text

These documents are publicly available on the Internet. They aretypically distributed together with the source code for AUTOCLASS byfile transfer from the Internet.

In the example of the present invention being discussed, the dataclassifier 21 is AutoClass. Data from the data source 22 is firstprepared for use by AutoClass as described in the documentpreparation-c. text, referred to above. In this example the data source22 comprises a list of 6 attribute values for each user of EDNA over acertain time period. This data is processed or formatted to meet therequirements specified in preparation-c. text. This involves creating anumber of files containing the data and other parameters specified bythe user.

The AutoClass is then used to classify the data as described insearch-c. text. The output of the data classifier 21, in this caseAutoClass, comprises:

(i) a set of classes 3 each of which is described by a set of classparameters, which specify how the class is distributed along the variousattributes;

(ii) a set of class weights describing what percentage of EDNA users arelikely to be in each class;

(iii) for each EDNA user, the relative probability that it is a memberof each class.

AutoClass repeatedly classifies the data to obtain several sets of suchresults which are then compared by the operator to determine the mostsuccessful classification(s).

When AutoClass is used various options can be chosen by the operator.These include parameters such as: specifying how many classes to lookfor or try; or specifying a maximum duration for the classification.Default values for these parameters are used unless the operatorspecifies otherwise. In the particular example being discussed, aboutEDNA, the parameter values were set so as to “look for” 20 classes.

However, it is not essential to use these exact parameter values. Indifferent situations different values may be more appropriate. Thevarious parameters are discussed in search-c. text as well as the otherdocuments referred to above.

The several sets of results are ranked by AUTOCLASS and one set ischosen. Typically the classification which describes the data mostcompletely is used. For example, this can be done by comparing the logtotal posterior probability value for each classification, as describedin interpretation-c.text. The results of the chosen classification canthen be analysed using AutoClass by generating simple reports whichdisplay the results in different ways. This is described inreports-c.text. One of the reports that is generated contains the rankedsets of results.

As mentioned earlier AutoClass is only one possible system that can beused for the data classifier 21. Any classification technique thatdetermines a set of classes for a given data set can be used.

In the AutoClass system, class membership is expressed probabilisticallyrather than as logical assignment. This is done by defining the classesin terms of parameterised probability distributions. Each example isconsidered to have a probability that it belongs to each of the classes.In this way, examples can be members of more than one class. However,each example must belong to at least one class because AutoClass makesthe class probabilities sum to 1.

Alternatively, clustering techniques could be used for the dataclassifier 21. Clustering techniques act to partition the data intoclasses so that each example is assigned to a class. For example, one ofthe basic known approaches is to form a least squares fit of the datapoints to a prespecified number of groupings. This requires that thenumber of clusters is known in advance, which is often not the case.Adaptive clustering techniques can also be used. These do not rely onpredefined parameters, for example see EP-A-0436913. Clusteringtechniques differ from the AutoClass and other Bayesian systems whichsearch in a model space for the “best” class descriptions. A bestclassification optimally trades off predictive accuracy against thecomplexity of the classes and does not “over fit” the data. Also, inAutoClass the classes are “fuzzy” so that an example can be a member ofmore than one class with different probabilities for membership.

Another possibility is to use a neural network classifier. This couldeither be unsupervised or supervised. Also, the neural network can beconfigured to give Bayesian probability outputs if desired using knowntechniques. Neural networks have a number of advantages that makes themparticularly suitable for use with information about the transmission ofmessages in a communications network and specifically fortelecommunications applications. These advantages include:

neural networks can be used to discover complex underlying patterns andanomalies in communications network data;

neural networks can learn both the normal and fraudulent communicationsbehaviour from examples;

neural networks can adapt to changes in the communications network data;

neural networks can perform data analysis on many different variables;

neural networks are excellent at performing pattern recognition tasks,such as detecting known behaviour types;

neural networks are more resilient than standard statistical techniquesto noisy training data;

neural network technology only requires to be retrained periodically;

a trained neural network is able to process new communications dataquickly.

Once a classification has been obtained, information about thisclassification 27 is combined with information about the data source 22to provide input for the rule inducer 25. For example, in the EDNAexample being discussed FIG. 6 shows the combined data ready for inputto the rule inducer 25. FIG. 6 shows a table of values 61 where each rowin the table is for an EDNA user. The first 6 columns of the table 61,show the attribute values as input to the data classifier 21. The lastcolumn 61 shows the class that each EDNA user has been classified intousing the data classifier 21.

Alternatively the output of the data classifier 21 may already be in aform similar to that shown in FIG. 6. That is, information from the datasource 22 may not need to be combined with the classification results27.

In the example being discussed, about EDNA, the system used for the ruleinducer 25 is CN2. This is a publicly available algorithm which isdescribed in the following documents which are intended to beincorporated herein by reference:

P. Clark and T. Niblett “The CN2 Induction Algorithm”, in MachineLearning Journal, 3 (4), pp 261-283, Netherlands: Cluner (1989)

P. Clark and R. Boswell. “Rule Induction with CN2: Some RecentImprovements,” In Machine Learning—Proceedings of the Fifth EuropeanConference (EWSL-91), pp 151-163, Ed: Y Kodratoff, Berlin: SpringerVerlag (1991).

R. Boswell “Manual for CN2 version 6.1” (1990) The Turing InstituteLimited IT/P2154/RAB/4/1-5

It is not essential to use the CN2 algorithm for the rule inducer 25;alternative rule induction techniques can be used. A rule inducer is ameans by which a rule-based system can learn by example. The process ofrule induction involves the creation of rules from a set of examples.The idea is to create rules which describe general concepts of theexample set. The term rule inducer is used here to refer to any systemwhich involves the creation of rules from a set of examples.

CN2 is a rule induction algorithm which takes a set of examples (thatare vectors of attribute values and information about which class eachexample is a member of) and generates a set of rules for classifyingthem. For example, a rule might take the form: “If telephone call number10 has attribute A and attribute B but not attribute C then it is amember of Class 2.”

In the example being discussed about EDNA, the rules take the form shownin FIG. 7. This shows 6 IF-THEN rules 71 and a default condition 72. Theattribute names 73 correspond to the attributes shown in FIG. 5 and therules specify various threshold valves for the attributes 74. Each rulehas a THEN portion specifying a membership of a particular Class 75. Thenumbers in square brackets 76 indicate how many examples met theconditions of the rule and were assigned to the particular class.

The rule inducer 25 may either be of a kind which produces an orderedsequence of rules or of a kind which produces an unordered sequence ofrules. When an ordered rule sequence is produced, then when the inducedrules are used to process new examples each rule is tried in order untilone is found whose conditions are satisfied. Order independent rulesrequire some additional mechanism to be provided to resolve any ruleconflicts which may occur. This has the disadvantage that a strictlogical interpretation of the rules is detracted from. However, orderedrules also sacrifice a degree of comprehensibility as the interpretationof a single rule is dependent on which other rules preceded it in thelist. The CN2 algorithm can be configured to produce either unordered orordered rules. In the particular example being discussed about EDNA, andunordered rule list was created.

The rules obtained from the rule inducer 25 are then evaluated. This isdone by comparing the information that was provided as input to the ruleinducer with the rules. For example in the EDNA situation input to therule inducer 25 took the form as shown in FIG. 6. For the first row inFIG. 6 the attribute value for sumofsumofnum pack is 27 and forsumofsumoffcomplete is 14 which satisfies the condition for the firstrule in FIG. 7. This example is assigned to Class C0 following theinduced rules, and from FIG. 6 we can see that it was also assigned toClass C0 by the data classifier. This type of evaluation can be carriedout automatically using the CN2 system as described in the CN2 Manualreferred to above.

When a successfully evaluated set of rules is obtained this can be usedto create a rule based classifier. For example, FIG. 8 shows a programwritten in the programming language PERL. This program incorporates therules from FIG. 7 that were produced by the rule inducer in the EDNAexample. For example 81 shows one such rule. When this program isexecuted new examples are classified into one of the classes C0, C1, C2or C3. In this way the program can be used to classify new examples ofEDNA use into one of these predetermined 4 classes. It is not essentialthat the programming language PERL is used; any suitable means forexecuting the rules can be used.

The rule based classifier shown in FIG. 8 is static and does not “learnby example”. That is, once the rules are induced and formed into theclassifier they are not altered automatically. However, it is possibleto incorporate the set of successfully evaluated rules into a moresophisticated rule based classifier. For example, this could be arrangedto learn from new examples that are presented to the classifier forclassification. Also the rules or computer system 1 can be incorporatedinto a case based reasoning system. In this type of system learning byanalogy plays an important role. Existing knowledge is applied to a newproblem instance on the basis of similarities between them. This caninvolve modifications of the existing knowledge to fit the new case. Acase based reasoning system typically has a case base which comprises aset of relevant examples. These cases are applied to new problems by ananalogical reasoning process.

A second example of the use of the invention is in detectingtelecommunications fraud. As shown in FIG. 4 call detail record data 41is input to an anomaly detector 42 which produces information aboutwhich of the call detail records are fraud candidates 43. The anomalydetector 42 comprises several components including a kernel 44 whichincorporates a neural network. This neural network is trained toclassify the input information 41 into classes indicating fraud ornon-fraud candidates 43. The neural network is thus equivalent to thedata classifier 21. A rule inducer 25, 45 is incorporated into theanomaly detector 42. The rule inducer 45 receives output informationfrom the neural network which comprises a set of attributes for eachcustomer account together with a class assignment for that customeraccount. The rule inducer then generates rules 24, 46.

The neural network can be of many possible types. For example, aself-organising map or a multi-layer perceptron. For example, if a selforganising map is used and the task is to detect telecommunicationsfraud many different classes may be produced by the neural networkclassifier. Some of these classes may related to known types of fraudand others to legitimate use. Still further classes may relate tounknown examples which could be new types of fraud or new types oflegitimate use. When a new type of fraud evolves it is important for theoperator to react to this quickly. A new “unknown” class may emerge inthe self organising map which could contain new types of fraud. By usingthe output of the rule inducer, or extractor, the operator can quicklyobtain information about the characteristics of the new class.

A wide range of applications are within the scope of the invention. Forexample, interpreting information relating to telecommunications fraud;credit card fraud; faults in a communications network and encryption keymanagement. The invention applies to any situation in which a largeamount of data needs to be analysed to extract features necessary for aparticular task or problem domain and where it is required to explain orinterpret the way in which the features were obtained. This can be usedfor knowledge engineering in the development of expert systems. Theinvention also applies to pattern recognition tasks for example taxonomyin biology, object recognition and object tracking.

What is claimed is:
 1. A computer product for analyzing datarepresentative of account usage, the computer product disposed on acomputer readable medium and including instructions for causing aprocessor to: provide to a data classifier the data representative ofaccount usage, wherein the data classifier produces classificationoutputs in response to the data representative of account usage, and,provide to a rule inducer, input data based on the data representativeof account usage, and the classification outputs, wherein the ruleinducer produces rules descriptive of relationships between the datarepresentative of account usage and the classification outputs.
 2. Acomputer product according to claim 1, wherein the data classifierincludes a neural network.
 3. A computer product according to claim 1,wherein the data classifier includes an unsupervised classifier.
 4. Acomputer product according to claim 1, wherein the data classifierincludes a Bayesian classifier.
 5. A computer product according to claim1, wherein the input data based on the data representative of accountusage includes a combination of the data representative of account usageand the classification outputs.
 6. A computer product according to claim1, further comprising instructions to cause the processor to use therules to provide a distinct second data classifier.